Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f6c2cbb8f60>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f515c94da90>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [3]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [4]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32,(None,image_width,image_height,image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32,(None,z_dim),name='input_z')
    learning_rate = tf.placeholder(tf.float32,name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False, alpha=0.1):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    keep_prob = 0.7
    
    with tf.variable_scope('discriminator',reuse=reuse):
        #Input layer is 28x28x3
        layer1 = tf.layers.conv2d(images, 32,5,strides=2,kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
        relu1 = tf.maximum(alpha * layer1, layer1)
        #print(relu1.get_shape())
        #14x14x64
        
        layer2 = tf.layers.conv2d(relu1,64,5,strides = 2, kernel_initializer=tf.contrib.layers.xavier_initializer(),padding='same')
        n_layer2 = tf.layers.batch_normalization(layer2, training = True)
        relu2 = tf.maximum(alpha * n_layer2, n_layer2)
        #print(relu2.get_shape())
        #7 x 7 x 128
        
        layer3 = tf.layers.conv2d(relu2,128,5,strides = 2, kernel_initializer=tf.contrib.layers.xavier_initializer(),padding='same')
        n_layer3 = tf.layers.batch_normalization(layer3, training = True)
        relu3 = tf.maximum(alpha * n_layer3, n_layer3)
        #print(relu3.get_shape())
        #4 x 4 x 256
        
        layer4 = tf.layers.conv2d(relu3,256,5,strides = 1, kernel_initializer=tf.contrib.layers.xavier_initializer(),padding='same')
        n_layer4 = tf.layers.batch_normalization(layer4, training = True)
        relu4 = tf.maximum(alpha * n_layer4, n_layer4)
        #print(relu3.get_shape())
        #4 x 4 x 512
        
        #Flatten it
        flat_layer = tf.reshape(relu4, (-1,4*4*256))
        logits = tf.layers.dense(flat_layer,1)
        logits = tf.nn.dropout(logits, keep_prob)
        out = tf.sigmoid(logits)
        
        
    

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [9]:
def generator(z, out_channel_dim, is_train=True, alpha = 0.1):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    keep_prob = 0.9
    
    with tf.variable_scope('generator', reuse=not is_train):
        layer1 = tf.layers.dense(z,7*7*512)
        layer1 = tf.nn.dropout(layer1, keep_prob)
        layer1 = tf.reshape(layer1, (-1,7,7,512))
        layer1 = tf.layers.batch_normalization(layer1, training=is_train)
        layer1 = tf.maximum(alpha*layer1, layer1)
        #print(layer1.get_shape())
        # 7 x 7 x 512
        
        layer2 = tf.layers.conv2d_transpose(layer1, 64, 5, strides=1,kernel_initializer=tf.contrib.layers.xavier_initializer(),padding='same')
        layer2 = tf.layers.batch_normalization(layer2, training=is_train)
        layer2 = tf.maximum(alpha*layer2, layer2)
        #print(layer2.get_shape())
        #7 x 7 X 256
        
        layer3 = tf.layers.conv2d_transpose(layer2, 32, 5, strides=2,kernel_initializer=tf.contrib.layers.xavier_initializer(),padding='same')
        layer3 = tf.layers.batch_normalization(layer3, training=is_train)
        layer3 = tf.maximum(alpha*layer3, layer3)
        #print(layer3.get_shape())
        #14 X 14 X 128
        
        layer4 = tf.layers.conv2d_transpose(layer3, 32, 5, strides=2,kernel_initializer=tf.contrib.layers.xavier_initializer(),padding='same')
        layer4 = tf.layers.batch_normalization(layer4, training=is_train)
        layer4 = tf.maximum(alpha*layer4, layer4)
        #print(layer4.get_shape())
        #28 x 28 x 64
        
        logits = tf.layers.conv2d_transpose(layer4, out_channel_dim, 5, strides=1,kernel_initializer=tf.contrib.layers.xavier_initializer(),padding='same')
        #print(logits.get_shape())
        # 28 x 28 x 5
        
        out = tf.tanh(logits)
        
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [22]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    smooth_deduct = 0.1
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1-smooth_deduct)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [23]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [24]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [33]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """


    input_real, input_z, learning_rate_h = model_inputs(data_shape[1],data_shape[2],data_shape[3],z_dim)
    d_loss, g_loss = model_loss(input_real,input_z,data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    

    print_every = 100
    steps = 0

    samples, losses = [], []
    
    sample_z = np.random.uniform(-1,1, size=(50,z_dim))
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                #normalize batch images
                batch_images = batch_images * 2
                steps += 1
                batch_z = np.random.uniform(-1,1, size=(batch_size, z_dim))
                _ = sess.run(d_opt, feed_dict={input_real:batch_images, input_z: batch_z, learning_rate_h:learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z:batch_z, learning_rate_h: learning_rate, input_real:batch_images})
                _ = sess.run(g_opt, feed_dict={input_z:batch_z, learning_rate_h: learning_rate, input_real:batch_images})
                
                
                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch{}/{}...".format(epoch_i+1,epoch_count), "Discriminator loss:{:.4f}...".format(train_loss_d),
                         "Generator loss: {:.4f}".format(train_loss_g))
                    
                    losses.append((train_loss_d, train_loss_g))
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
                    
    
        saver = tf.train.Saver()
        saver.save(sess, 'gan-model')
    
    
        
                    
                    
                
                
              

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [30]:
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.55


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch1/2... Discriminator loss:1.6910... Generator loss: 0.6461
Epoch1/2... Discriminator loss:1.6759... Generator loss: 0.6811
Epoch1/2... Discriminator loss:1.5097... Generator loss: 0.5788
Epoch1/2... Discriminator loss:1.9084... Generator loss: 0.4008
Epoch1/2... Discriminator loss:1.5662... Generator loss: 0.6633
Epoch1/2... Discriminator loss:1.3216... Generator loss: 0.6630
Epoch1/2... Discriminator loss:1.4948... Generator loss: 0.8204
Epoch1/2... Discriminator loss:1.4428... Generator loss: 0.7386
Epoch1/2... Discriminator loss:1.6538... Generator loss: 0.4871
Epoch1/2... Discriminator loss:1.6776... Generator loss: 0.3886
Epoch1/2... Discriminator loss:1.5948... Generator loss: 0.5044
Epoch1/2... Discriminator loss:1.7239... Generator loss: 0.4650
Epoch1/2... Discriminator loss:1.3391... Generator loss: 0.9299
Epoch1/2... Discriminator loss:1.3868... Generator loss: 0.8313
Epoch1/2... Discriminator loss:1.3201... Generator loss: 0.6594
Epoch1/2... Discriminator loss:1.5879... Generator loss: 0.4036
Epoch1/2... Discriminator loss:1.5766... Generator loss: 0.5069
Epoch1/2... Discriminator loss:1.8866... Generator loss: 0.3362
Epoch2/2... Discriminator loss:1.7652... Generator loss: 1.4696
Epoch2/2... Discriminator loss:1.4609... Generator loss: 0.5332
Epoch2/2... Discriminator loss:1.7228... Generator loss: 0.7771
Epoch2/2... Discriminator loss:1.6587... Generator loss: 0.5534
Epoch2/2... Discriminator loss:1.6544... Generator loss: 0.4893
Epoch2/2... Discriminator loss:1.6207... Generator loss: 0.4171
Epoch2/2... Discriminator loss:1.9861... Generator loss: 0.3130
Epoch2/2... Discriminator loss:1.5411... Generator loss: 0.5444
Epoch2/2... Discriminator loss:1.2024... Generator loss: 0.7340
Epoch2/2... Discriminator loss:1.4512... Generator loss: 0.5105
Epoch2/2... Discriminator loss:1.7201... Generator loss: 0.4768
Epoch2/2... Discriminator loss:1.4203... Generator loss: 0.7429
Epoch2/2... Discriminator loss:1.3042... Generator loss: 0.8050
Epoch2/2... Discriminator loss:1.4563... Generator loss: 0.5844
Epoch2/2... Discriminator loss:1.6760... Generator loss: 0.3667
Epoch2/2... Discriminator loss:1.7686... Generator loss: 0.4132
Epoch2/2... Discriminator loss:1.6867... Generator loss: 0.4351
Epoch2/2... Discriminator loss:1.3668... Generator loss: 0.6128
Epoch2/2... Discriminator loss:1.2535... Generator loss: 0.7662

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [34]:
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.55


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch1/1... Discriminator loss:1.6392... Generator loss: 0.7723
Epoch1/1... Discriminator loss:1.5746... Generator loss: 0.4912
Epoch1/1... Discriminator loss:1.3791... Generator loss: 0.9184
Epoch1/1... Discriminator loss:1.3791... Generator loss: 0.8869
Epoch1/1... Discriminator loss:1.7764... Generator loss: 0.8723
Epoch1/1... Discriminator loss:1.3541... Generator loss: 0.7077
Epoch1/1... Discriminator loss:1.6078... Generator loss: 0.5841
Epoch1/1... Discriminator loss:1.5465... Generator loss: 0.6797
Epoch1/1... Discriminator loss:1.4279... Generator loss: 0.7459
Epoch1/1... Discriminator loss:1.4063... Generator loss: 0.8652
Epoch1/1... Discriminator loss:1.5259... Generator loss: 0.7320
Epoch1/1... Discriminator loss:1.5326... Generator loss: 0.5650
Epoch1/1... Discriminator loss:1.4293... Generator loss: 0.6885
Epoch1/1... Discriminator loss:1.3864... Generator loss: 0.7126
Epoch1/1... Discriminator loss:1.5467... Generator loss: 0.6701
Epoch1/1... Discriminator loss:1.5357... Generator loss: 0.7274
Epoch1/1... Discriminator loss:1.4813... Generator loss: 0.7701
Epoch1/1... Discriminator loss:1.3782... Generator loss: 0.7737
Epoch1/1... Discriminator loss:1.5761... Generator loss: 0.6189
Epoch1/1... Discriminator loss:1.5248... Generator loss: 0.6947
Epoch1/1... Discriminator loss:1.5412... Generator loss: 0.6001
Epoch1/1... Discriminator loss:1.5744... Generator loss: 0.8352
Epoch1/1... Discriminator loss:1.5012... Generator loss: 0.6308
Epoch1/1... Discriminator loss:1.5362... Generator loss: 0.5625
Epoch1/1... Discriminator loss:1.5243... Generator loss: 0.8189
Epoch1/1... Discriminator loss:1.5250... Generator loss: 0.6211
Epoch1/1... Discriminator loss:1.4108... Generator loss: 0.5989
Epoch1/1... Discriminator loss:1.4431... Generator loss: 0.7550
Epoch1/1... Discriminator loss:1.4621... Generator loss: 0.7295
Epoch1/1... Discriminator loss:1.5229... Generator loss: 0.5671
Epoch1/1... Discriminator loss:1.4888... Generator loss: 0.6493
Epoch1/1... Discriminator loss:1.4328... Generator loss: 0.6881
Epoch1/1... Discriminator loss:1.5372... Generator loss: 0.5780
Epoch1/1... Discriminator loss:1.4499... Generator loss: 0.6462
Epoch1/1... Discriminator loss:1.3936... Generator loss: 0.7065
Epoch1/1... Discriminator loss:1.4857... Generator loss: 0.8409
Epoch1/1... Discriminator loss:1.4254... Generator loss: 0.7521
Epoch1/1... Discriminator loss:1.4927... Generator loss: 0.8103
Epoch1/1... Discriminator loss:1.4728... Generator loss: 0.7198
Epoch1/1... Discriminator loss:1.3940... Generator loss: 0.7401
Epoch1/1... Discriminator loss:1.4322... Generator loss: 0.5455
Epoch1/1... Discriminator loss:1.4436... Generator loss: 0.6652
Epoch1/1... Discriminator loss:1.3678... Generator loss: 0.7159
Epoch1/1... Discriminator loss:1.4963... Generator loss: 0.6600
Epoch1/1... Discriminator loss:1.3906... Generator loss: 0.7661
Epoch1/1... Discriminator loss:1.4802... Generator loss: 0.6112
Epoch1/1... Discriminator loss:1.5370... Generator loss: 0.6086
Epoch1/1... Discriminator loss:1.3917... Generator loss: 0.6671
Epoch1/1... Discriminator loss:1.4492... Generator loss: 0.7300
Epoch1/1... Discriminator loss:1.4137... Generator loss: 0.6471
Epoch1/1... Discriminator loss:1.5294... Generator loss: 0.9175
Epoch1/1... Discriminator loss:1.4517... Generator loss: 0.6461
Epoch1/1... Discriminator loss:1.5551... Generator loss: 0.6639
Epoch1/1... Discriminator loss:1.4827... Generator loss: 0.5879
Epoch1/1... Discriminator loss:1.4769... Generator loss: 0.6110
Epoch1/1... Discriminator loss:1.4748... Generator loss: 0.8166
Epoch1/1... Discriminator loss:1.4064... Generator loss: 0.6615
Epoch1/1... Discriminator loss:1.4098... Generator loss: 0.6650
Epoch1/1... Discriminator loss:1.4427... Generator loss: 0.6587
Epoch1/1... Discriminator loss:1.4083... Generator loss: 0.6652
Epoch1/1... Discriminator loss:1.3858... Generator loss: 0.7388
Epoch1/1... Discriminator loss:1.5154... Generator loss: 0.5797
Epoch1/1... Discriminator loss:1.4269... Generator loss: 0.6334

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.